Appropriate statistical methods for analysis of safflower genetic diversity using agglomerative hierarchical cluster analysis through combination of phenotypic traits and molecular markers

Crop Science ◽  
2021 ◽  
Author(s):  
Karim Houmanat ◽  
Ahmed Douaik ◽  
Jamal Charafi ◽  
Lahcen Hssaini ◽  
Mohamed El Fechtali ◽  
...  
Keyword(s):  
Genetic Diversity ◽  
Cluster Analysis ◽  
Molecular Markers ◽  
Statistical Methods ◽  
Hierarchical Cluster Analysis ◽  
Hierarchical Cluster ◽  
Phenotypic Traits ◽  
Agglomerative Hierarchical Cluster ◽  
Agglomerative Hierarchical Cluster Analysis

scholarly journals A COMPARISON BETWEEN SINGLE LINKAGE AND COMPLETE LINKAGE IN AGGLOMERATIVE HIERARCHICAL CLUSTER ANALYSIS FOR IDENTIFYING TOURISTS SEGMENTS

2012 ◽  
Vol 12 (6) ◽  
Author(s):  
Noor Rashidah Rashid
Keyword(s):  
Cluster Analysis ◽  
Hierarchical Cluster Analysis ◽  
Hierarchical Cluster ◽  
Single Linkage ◽  
Complete Linkage ◽  
Multivariate Method ◽  
Agglomerative Hierarchical Cluster ◽  
Linkage Methods ◽  
Agglomerative Hierarchical Cluster Analysis ◽  
Analyze Data

Cluster Analysis is a multivariate method in statistics. Agglomerative Hierarchical Cluster Analysis is one of approaches in Cluster Analysis. There are two linkage methods in Agglomerative Hierarchical Cluster Analysis which are Single Linkage and Complete Linkage. The purpose of this study is to compare between Single Linkage and Complete Linkage in Agglomerative Hierarchical Cluster Analysis. The comparison of performances between these linkage methods was shown by using Kruskal-Wallis test. The result of the comparison used for segmenting tourists of Kapas Island. The statistical software SPSS has been applied to analyze data of this research. The result from Kruskal-Wallis test shows Complete Linkage is more useful in identifying tourists segments. Keywords : Agglomerative Hierarchical Cluster Analysis, Single Linkage, Complete Linkage, Kruskal-Wallis test, tourists

scholarly journals The Human Resources as an Important Factor of Regional Development

2021 ◽  
Vol 21 (3) ◽  
pp. 1464-1478
Author(s):  
Dana Jašková ◽  
Katarína Havierniková ◽  
Katarína Havierniková
Keyword(s):  
Cluster Analysis ◽  
Human Resources ◽  
Statistical Methods ◽  
Hierarchical Cluster Analysis ◽  
Socioeconomic Development ◽  
Slovak Republic ◽  
Hierarchical Cluster ◽  
Human Resources Development ◽  
Development Potential ◽  
Resources Development

The human resources present the key aspect of socioeconomic development, prosperity and competitiveness of each region due to the fact that contribute to increasing of employment, to the development of working places and improving the life standard of inhabitants. The main aim of the paper is the quantification of human resources development potential in Slovak regions. To meet this aim, the authors concentrated attention mainly on the evaluation of the level and variability of the selected indicators that were observed within four descriptors. The crucial contribution of the evaluation is the ranking of regions, based on the evaluation in each descriptor and drawing the overall ranking of human resources development potential in the Slovak Republic. To determine the order of regions, the relevant statistical methods were used: the characteristics of location and variability, hierarchical cluster analysis and its method of complete linkage. The results showed the dominance of Bratislava region that is following by Trnava and Žilina region.

scholarly journals Implications of COVID-19 vaccination and public health countermeasures on SARS-CoV-2 variants of concern in Canada: evidence from a spatial hierarchical cluster analysis

2021 ◽  
Author(s):  
Daniel A Adeyinka ◽  
Cheryl Camillo ◽  
Wendie Marks ◽  
Nazeem Muhajarine
Keyword(s):  
Public Health ◽  
Cluster Analysis ◽  
Hierarchical Cluster Analysis ◽  
Vaccine Coverage ◽  
Hierarchical Cluster ◽  
Vaccine Uptake ◽  
Mobility Index ◽  
Agglomerative Hierarchical Cluster Analysis ◽  
Mitigating Factors ◽  
Central Canada

Background: The influence of coronavirus disease-2019 (COVID-19) containment measures on variants of concern (VOC) has been understudied in Canada. Our objective was to identify provinces with disproportionate prevalence of VOC relative to COVID-19 mitigation efforts in provinces and territories in Canada. Methods: We analyzed publicly available provincial- and territorial-level data on the prevalence of VOCs in relation to mitigating factors (summarized in three measures: 1. strength of public health countermeasures: stringency index, 2. how much people moved about outside their homes: mobility index, and 3. vaccine intervention: proportion of Canadian population fully vaccinated). Using spatial agglomerative hierarchical cluster analysis (unsupervised machine learning), the provinces and territories were grouped into clusters by stringency index, mobility index and full vaccine coverage. Kruskal-Wallis test was used to determine the differences in the prevalence of VOC (Alpha, or B.1.1.7, Beta, or B.1.351, Gamma, or P.1, and Delta, or B.1.617.2 variants) between the clusters. Results: Three clusters of vaccine uptake and countermeasures were identified. Cluster 1 consisted of the three Canadian territories, and characterized by higher degree of vaccine deployment and lesser degree of countermeasures. Cluster 2 (located in Central Canada and Atlantic region) was typified by lesser implementation of vaccine deployment and moderate countermeasures. The third cluster was formed by provinces inthe Pacific region, Central Canada, and Prairie region, with moderate vaccine deployment but stronger countermeasures. The overall and variant-specific prevalence were significantly different across the clusters. Interpretation: This study found that implementation of COVID-19 public health measures varied across the provinces and territories. Considering the high prevalence of VOCs in Canada, completing the second dose of COVID-19 vaccine in a timely manner is crucial.

Extraordinary circumstances: Covid-19 – Italian Serie A scenario

2021 ◽  
pp. 175433712110192
Author(s):  
Milan Radojicic ◽  
Aleksandar Djokovic ◽  
Nikola Cvetkovic
Keyword(s):  
Cluster Analysis ◽  
Hierarchical Cluster Analysis ◽  
Hierarchical Cluster ◽  
Round Robin ◽  
The Past ◽  
Novel Approach ◽  
Round Robin Tournaments

Unpredictable and uncontrollable situations have happened throughout history. Inevitably, such situations have an impact on various spheres of life. The coronavirus disease 2019 has affected many of them, including sports. The ban on social gatherings has caused the cancellation of many sports competitions. This paper proposes a methodology based on hierarchical cluster analysis (HCA) that can be applied when a need occurs to end an interrupted tournament and the conditions for playing the remaining matches are far from ideal. The proposed methodology is based on how to conclude the season for Serie A, a top-division football league in Italy. The analysis showed that it is reasonable to play 14 instead of the 124 remaining matches of the 2019–2020 season to conclude the championship. The proposed methodology was tested on the past 10 seasons of the Serie A, and its effectiveness was confirmed. This novel approach can be used in any other sport where round-robin tournaments exist.

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